Automated Author ProfileLiao, Can
Liao, Can
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Purpose: The objectives of this study were to explore genetics pathogenesis of isolated agenesis of corpus callosum (ACC) and assess the utility of chromosomal microarray analysis (CMA) for genetic diagnosis of isolated ACC. Methods: We analyzed the genomes of 16 fetuses with isolated ACC using Afymetrix CytoScan HD arrays and conducted further bioinformatic analysis for one proband fetus with an abnormal copy number variation (CNV). Results: Of the 16 fetal samples examined, two (12.5%) had pathogenic CNVs and three (18.75%) had variants of unknown significance. Two cases, case 2 and case 9, were found to have pathogenic CNVs. Bioinformatic analyses indicated that the CNV of one fetus (case 9) contained 115 annotated coding genes, five of which (SLC6A5, BDNF, ELP4, PAX6, and SLC1A2) have been associated with neurodevelopment. Three of these genes (SLC1A2, BDNF, and PAX6) may play a key role in ACC development. GO cluster analysis of the implicated genes revealed strong representations of protein binding and metal ion binding functions. KEGG pathway analysis pointed to four pathways: longevity regulating pathway, amyotrophic lateral sclerosis, cocaine addiction, and autophagy-animal. Conclusions: BDNF, SLC1A2, and PAX6 may be involved in the development of isolated ACC. CMA is a feasible technology for prenatal diagnosis of isolated ACC.
Authors
- She, Qin ;
- Fu, Fang ;
- Xiaoyan Guo ;
- Weihe Tan ;
- Liao, Can
Purpose: The objectives of this study were to explore genetics pathogenesis of isolated agenesis of corpus callosum (ACC) and assess the utility of chromosomal microarray analysis (CMA) for genetic diagnosis of isolated ACC. Methods: We analyzed the genomes of 16 fetuses with isolated ACC using Afymetrix CytoScan HD arrays and conducted further bioinformatic analysis for one proband fetus with an abnormal copy number variation (CNV). Results: Of the 16 fetal samples examined, two (12.5%) had pathogenic CNVs and three (18.75%) had variants of unknown significance. Two cases, case 2 and case 9, were found to have pathogenic CNVs. Bioinformatic analyses indicated that the CNV of one fetus (case 9) contained 115 annotated coding genes, five of which (SLC6A5, BDNF, ELP4, PAX6, and SLC1A2) have been associated with neurodevelopment. Three of these genes (SLC1A2, BDNF, and PAX6) may play a key role in ACC development. GO cluster analysis of the implicated genes revealed strong representations of protein binding and metal ion binding functions. KEGG pathway analysis pointed to four pathways: longevity regulating pathway, amyotrophic lateral sclerosis, cocaine addiction, and autophagy-animal. Conclusions: BDNF, SLC1A2, and PAX6 may be involved in the development of isolated ACC. CMA is a feasible technology for prenatal diagnosis of isolated ACC.
Authors
- She, Qin ;
- Fu, Fang ;
- Xiaoyan Guo ;
- Weihe Tan ;
- Liao, Can